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Human Cysteine Cathepsins Degrade Immunoglobulin G In Vitro in a Predictable Manner

Cysteine cathepsins are critical components of the adaptive immune system involved in the generation of epitopes for presentation on human leukocyte antigen (HLA) molecules and have been implicated in degradation of autoantigens. Immunoglobulin variable regions with somatic mutations and random comp...

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Detalles Bibliográficos
Autores principales: Høglund, Rune Alexander, Torsetnes, Silje Bøen, Lossius, Andreas, Bogen, Bjarne, Homan, E. Jane, Bremel, Robert, Holmøy, Trygve
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6801702/
https://www.ncbi.nlm.nih.gov/pubmed/31569504
http://dx.doi.org/10.3390/ijms20194843
Descripción
Sumario:Cysteine cathepsins are critical components of the adaptive immune system involved in the generation of epitopes for presentation on human leukocyte antigen (HLA) molecules and have been implicated in degradation of autoantigens. Immunoglobulin variable regions with somatic mutations and random complementarity region 3 amino acid composition are inherently immunogenic. T cell reactivity towards immunoglobulin variable regions has been investigated in relation to specific diseases, as well as reactivity to therapeutic monoclonal antibodies. Yet, how the immunoglobulins, or the B cell receptors, are processed in endolysosomal compartments of professional antigen presenting cells has not been described in detail. Here we present in silico and in vitro experimental evidence suggesting that cysteine cathepsins S, L and B may have important roles in generating peptides fitting HLA class II molecules, capable of being presented to T cells, from monoclonal antibodies as well as from central nervous system proteins including a well described autoantigen. By combining neural net models with in vitro proteomics experiments, we further suggest how such degradation can be predicted, how it fits with available cellular models, and that it is immunoglobulin heavy chain variable family dependent. These findings are relevant for biotherapeutic drug design as well as to understand disease development. We also suggest how these tools can be improved, including improved machine learning methodology.